Human psychology towards risk is complex and multi-dimensional; true 'rationality' in decision-making is a myth, and embracing uncertainty is more effective than trying to eliminate it.
Statistical process control is a powerful tool for public safety, proven by its ability to detect anomalies like the crimes of serial killer Harold Shipman and its current use in monitoring adverse events in UK maternity units.
A major paradigm shift is occurring in predictive fields like weather forecasting, where data-driven 'black box' AI models are competing with, and sometimes outperforming, traditional physics-based models, raising questions about accuracy versus interpretability.
For both individuals and organizations, the most effective strategy for navigating 'deep uncertainty'—where future possibilities are unknown—is to cultivate resilience rather than attempting to predict every outcome.
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Concerns Raised
The over-reliance on 'black box' AI models that lack explanatory power and can't quantify uncertainty.
The myth of 'rational' decision-making, which leads to flawed models of human behavior and risk.
The weaponization of statistics in public discourse without acknowledging the human judgment embedded in them.
The difficulty in obtaining good long-term survival data for newer medical treatments, creating uncertainty for patients.
Opportunities Identified
Applying statistical process control more broadly in healthcare to improve patient safety and detect malpractice early.
Leveraging the complementary strengths of both traditional and AI-based models in complex fields like weather forecasting.
Improving public understanding and communication of risk and uncertainty to foster more resilient societies.
Developing personal and organizational resilience as the primary strategy for navigating deep, unquantifiable uncertainty.